Lessons in Game Design, lecture by Will Wright

Earlier HN discussions about Factorio: Factorio 1.0 link link >Friday Facts #360 - 1.0 is here! link >Factorio (and also games like SimCity) are not actually pure CA rules, but they combine cellular automata techniques together with many other techniques like system dynamics, etc. Will Wright gave a great explanation of how simulation games combine different techniques together in three intersection dimensions: Topologies (agents, networks, and layers), dynamics (propagation, growth, grouping, order, allocation, mapping, specialization, and nesting), and paradigms (cybernetics, system dynamics, cellular automata, chaos theory, adaptive systems, network theory). ▶ this video >Lessons in Game Design, lecture by Will Wright, Computer History Museum, November 20, 2003. Understanding Kafka with Factorio (hackernoon.com) link >When I saw this title, I first thought it was about understanding the cockroach-like aliens in Factorio, but it is actually about queuing and congestion and parallel processing with conveyor belts and factories! link Von Neumann Universal Constructor (wikipedia.org) link link DonHopkins 10 months ago | on: Von Neumann Universal Constructor link >Factorio players will recognize these tapes of construction instructions as 2D "blueprints" that construction drones use to build patterns of factories and conveyor belts, etc. In Factorio, after your drones have build a blueprint in the unpowered, unsupplied state, you can connect it to the power grid, hook up pipes to deliver fluids, and run conveyor belts in and out of it to deliver resources and products, and it will immediately starts doing its thing. Playing Factorio is uncannily like von Neumann 29 state cellular automata programming, not by coincidence. So it's a great way to get your head around cellular automata programming, gpu programming, parallel programming, queuing systems, and data flow programming in general! link >Here's some stuff about that I posted in an earlier discussion, and transcribed from John von Neumann's book, "Theory of Self-Reproducing Automata". >His concept of self-reproducing mutating probabilistic quantum mechanical machine evolution is quite fascinating and terrifying at the same time (or outside of time), potentially much more powerful and dangerous than mere physical nanotechnology "gray goo" and universe-infesting self replicating von Neumann probes:

Factorio (and also games like SimCity) are not actually pure CA rules, but they combine cellular automata techniques together with many other techniques like system dynamics, etc. Will Wright gave a great explanation of how simulation games combine different techniques together in three intersection dimensions: Topologies (agents, networks, and layers), dynamics (propagation, growth, grouping, order, allocation, mapping, specialization, and nesting), and paradigms (cybernetics, system dynamics, cellular automata, chaos theory, adaptive systems, network theory). ▶ this video >Lessons in Game Design, lecture by Will Wright, Computer History Museum, November 20, 2003. Maybe the comment I posted about Factorio and CA was this, in the discussion of John von Neumann's 29 state cellular automata rule -- It is a pure CA, and a historically interesting one that he actually designed on paper and wrote about in a book. I compared it to Factorio, in the way Factorio uses conveyor belts in four different directions to direct the flow of items, and JVN29 uses arrows in four different directions to direct the flow of signals. But you can put a lot of different kinds of things on Factorio conveyor belts, but only ones and zeros on JVN29 arrows, since it was designed to be minimal and mathematically rigorous like a Turing Machine, not practical and convenient to program and fun to play like Factorio. link >Von Neumann Universal Constructor (wikipedia.org) 90 points by amjd 4 months ago | hide | past | favorite | 29 comments link link >[...] >Factorio players will recognize these tapes of construction instructions as 2D "blueprints" that construction drones use to build patterns of factories and conveyor belts, etc. In Factorio, after your drones have build a blueprint in the unpowered, unsupplied state, you can connect it to the power grid, hook up pipes to deliver fluids, and run conveyor belts in and out of it to deliver resources and products, and it will immediately starts doing its thing. Playing Factorio is uncannily like von Neumann 29 state cellular automata programming, not by coincidence. So it's a great way to get your head around cellular automata programming, gpu programming, parallel programming, queuing systems, and data flow programming in general! >Factorio Tutorial #20 - Bots, part 1 - Construction robots link >Factorio just doesn't have the ability to construct cells by spilling items off the end of conveyor belts, or destroy cells with conveyor belts, either. But maybe there's an extension for that! And John von Neumann's 29 state cellular automata doesn't have swarms of construction drones that build and tear down blueprints in parallel like Factorio does, so there are some differences. But the basic idea of grids of cells with conveyor belts carrying items between factories is the same. Also I wrote some more about JVN32: link >The Real Time Crossing (Buckley, p. 457, The real-time crossing organ) is like a road intersection that splits the two crossing lanes, then uses traffic lights to give cars in each pair of lanes alternating turns to cross, and then merges the lanes back together (since each intersection works at 50% throughput, you need to split, use two of them, and merge -- Factorio and Satisfactory players will get what I mean, in terms of conveyor belts, splitters and mergers, and conveyor belt throughput). Also I asked Alan Kay about Factorio and other games here, and he replied: link >DonHopkins on May 31, 2016 | parent | favorite | on: Alan Kay's reading list >I've heard you say that Rocky's Boots was one of your favorite computer games. Please, off the top of your head, what's your

If you've got about an hour and a half to spend, and you enjoyed this article, I'd like to recommend a lecture given by Will Wright in 2003. He covers his background, early computer games, the origins of Sim City, the Sims, as well as more abstract/high-level discussion of game design and simulation. Some high level themes of the talk: using the computer as a modeling tool, simulation design, Will Wright's game design principles and philosophy, programming for two dynamic processes "the software" and "the player's model of the system", and traversal of possibility spaces in game design and simulation. ▶ this video

That's not what Minsky or Sloman said they believed, nor what I meant to imply. link Minsky: What is the answer? My opinion is that we can make versatile AI machines only by using several different kinds of representations in the same system! This is because no single method works well for all problems; each is good for certain tasks but not for others. Also different kinds of problems need different kinds of reasoning. For example, much of the reasoning used in computer programming can be logic-based. However, most real-world problems need methods that are better at matching patterns and constructing analogies, making decisions based on previous experience with examples, or using types of explanations that have worked well on similar problems in the past. How can we encourage people to make systems that use multiple methods for representing and reasoning? First we'll have to change some present-day ideas. For example, many students like to ask, "Is it better to represent knowledge with Neural Nets, Logical Deduction, Semantic Networks, Frames, Scripts, Rule-Based Systems or Natural Language?" My teaching method is to try to get them to ask a different kind of question. "First decide what kinds of reasoning might be best for each different kind of problem -- and then find out which combination of representations might work well in each case." A trick that might help them to start doing this is to begin by asking, for each problem, "How many different factors are involved, and how much influence does each factor have?" This leads to a sort of "theory-matrix." link Sloman: In retrospect, it seems that a mixture of the probabilistic and deterministic approaches is required, within the study of architectures for complete agents: a more general study than the investigation of algorithms and representations that dominated most of the early work on AI (partly because of the dreadful limitations of speed and memory of even the most expensive and sophisticated computers available in the 1960s and 1970s). There are many ways such hybrid mechanisms could be implemented, and my recent work on different processing layers within an integrated architecture (combining reactive, deliberative and meta-management layers) indicates some features of a hybrid system, with probabilistic associations dominating the reactive layer and structure manipulations being more important in the deliberative layer. For recent papers on this see - The Cogaff papers directory link - My "talks" directory: link - The rapidly growing "miscellaneous" directory: link More specific though less comprehensive models have been proposed by other researchers, one of the most impressive being the ACT-R system developed by John Anderson and his collaborators. See link Added 8 Feb 2016 Minsky's paper on "causal diversity" is also relevant: Marvin L. Minsky, 1992, Future of AI Technology, in Toshiba Review, 47, 7, link ---- Will Wright discusses how he applies several different kinds of representations to make hybrid models for games, in the democratically elected "Dynamics" section of his talk, "Lessons in Game Design", on "nested dynamics / emergence" and "paradigms". ▶ this video